notebooks/3_2_inventor_overview_academia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9861ef9d",
   "metadata": {},
   "source": [
    "### Overview of the patent assignees/owners belonging to academia for each disease\n",
    "\n",
    "#### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec7518da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from collections import defaultdict\n",
    "import string\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61443f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b550e79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 400"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96020194",
   "metadata": {},
   "source": [
    "### Loading data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bed58e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = '../data/patent_dumps'\n",
    "FIG_DIR = '../data/figures'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7be5f940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chembl</th>\n",
       "      <th>surechembl</th>\n",
       "      <th>patent_id</th>\n",
       "      <th>date</th>\n",
       "      <th>ipc</th>\n",
       "      <th>assignee</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>acquired_by</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL3137316</td>\n",
       "      <td>SCHEMBL1707427</td>\n",
       "      <td>US-20140316139-A1</td>\n",
       "      <td>2014-10-23</td>\n",
       "      <td>C07D 213/89</td>\n",
       "      <td>PFIZER</td>\n",
       "      <td>Pfizer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL3137316</td>\n",
       "      <td>SCHEMBL1707427</td>\n",
       "      <td>US-20140142316-A1</td>\n",
       "      <td>2014-05-22</td>\n",
       "      <td>C07D 213/89</td>\n",
       "      <td>PFIZER</td>\n",
       "      <td>Pfizer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          chembl      surechembl          patent_id        date          ipc  \\\n",
       "0  CHEMBL3137316  SCHEMBL1707427  US-20140316139-A1  2014-10-23  C07D 213/89   \n",
       "1  CHEMBL3137316  SCHEMBL1707427  US-20140142316-A1  2014-05-22  C07D 213/89   \n",
       "\n",
       "  assignee organization_name acquired_by person  \n",
       "0   PFIZER            Pfizer         NaN    NaN  \n",
       "1   PFIZER            Pfizer         NaN    NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_disease_data = pd.read_csv(f'{DATA_DIR}/orphanet_normalized_patent_data.tsv', sep='\\t')\n",
    "rare_disease_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b130a7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chembl</th>\n",
       "      <th>surechembl</th>\n",
       "      <th>patent_id</th>\n",
       "      <th>date</th>\n",
       "      <th>ipc</th>\n",
       "      <th>assignee</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>acquired_by</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL1460470</td>\n",
       "      <td>SCHEMBL14919618</td>\n",
       "      <td>US-20140256767-A1</td>\n",
       "      <td>2014-09-11</td>\n",
       "      <td>C07D 401/14</td>\n",
       "      <td>UNIV RUTGERS</td>\n",
       "      <td>Rutgers University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL1460470</td>\n",
       "      <td>SCHEMBL14919618</td>\n",
       "      <td>US-20180148408-A1</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>C07D 209/48</td>\n",
       "      <td>UNIV RUTGERS</td>\n",
       "      <td>Rutgers University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          chembl       surechembl          patent_id        date          ipc  \\\n",
       "0  CHEMBL1460470  SCHEMBL14919618  US-20140256767-A1  2014-09-11  C07D 401/14   \n",
       "1  CHEMBL1460470  SCHEMBL14919618  US-20180148408-A1  2018-05-31  C07D 209/48   \n",
       "\n",
       "       assignee   organization_name acquired_by person  \n",
       "0  UNIV RUTGERS  Rutgers University         NaN    NaN  \n",
       "1  UNIV RUTGERS  Rutgers University         NaN    NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alzheimer_disease_data = pd.read_csv(f'{DATA_DIR}/ad_normalized_patent_data.tsv', sep='\\t')\n",
    "alzheimer_disease_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238dc223",
   "metadata": {},
   "source": [
    "Drop patents with no assignee names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6391a0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17506, 9), (76321, 9))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_disease_data.shape, alzheimer_disease_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02ca7c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17375, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_disease_data = rare_disease_data[~rare_disease_data['assignee'].isna()]\n",
    "rare_disease_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06689a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75616, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alzheimer_disease_data = alzheimer_disease_data[~alzheimer_disease_data['assignee'].isna()]\n",
    "alzheimer_disease_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de8a531",
   "metadata": {},
   "source": [
    "### Historic perspective of academic vs industry assignees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb19d69",
   "metadata": {},
   "source": [
    "Patent document based assignee ranking "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc6da2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "UNIVERSITY_ASSIGNEES = {\n",
    "    'National Yang Ming Chiao Tung University',\n",
    "    'KYOTO University',\n",
    "    'Jacobs University',\n",
    "    'The Scripps Research Institute',\n",
    "    'Goethe University Frankfurt',\n",
    "    'Northwestern University',\n",
    "    'Helmholtz Zentrum Munchen - German Research Center for Environmental Health',\n",
    "    'Atilim University',\n",
    "    'Vanderbilt University',\n",
    "    'University of Siena',\n",
    "    'North Carolina Central University',\n",
    "    'Hanyang University',\n",
    "    'Portland State University',\n",
    "    'University of Oslo',\n",
    "    'National Taiwan Normal University',\n",
    "    'University of Strasbourg',\n",
    "    'University of Oviedo',\n",
    "    'Yonsei University Industry Academic Cooperation Foundation',\n",
    "    'Virginia Commonwealth University',\n",
    "    'University of Georgia',\n",
    "    'University of Tennessee Research Foundation',\n",
    "    'University of Copenhagen',\n",
    "    'McGill University',\n",
    "    'Ludwig Maximilian University of Munich',\n",
    "    'University of Allahabad',\n",
    "    'Laval University',\n",
    "    'Boston University',\n",
    "    'Osaka University',\n",
    "    'Institute of Molecular Biotechnology of the OeAW',\n",
    "    'University of Barcelona',\n",
    "    'Helmholtz Centre for Infection Research',\n",
    "    'National Institute of Biological Sciences',\n",
    "    'Medical Center of Postgraduate Education',\n",
    "    'Sagami Chemical Research Institute (SCRI)',\n",
    "    'University Health Network',\n",
    "    'Oregon State University',\n",
    "    'University of California',\n",
    "    'University of Louisville Research Foundation',\n",
    "    'Tianjin Institute of Pharmaceutical Research',\n",
    "    'The University of Edinburgh',\n",
    "    'Saarland University',\n",
    "    'University of Liege',\n",
    "    'Palacky University Olomouc',\n",
    "    'Leiden University',\n",
    "    'The University of British Columbia',\n",
    "    'University of Arizona',\n",
    "    'Dana-Farber Cancer Institute',\n",
    "    'University of Warsaw',\n",
    "    'Indiana University Research And Technology Corporation',\n",
    "    'Jagiellonian University',\n",
    "    'Aix-Marseille University',\n",
    "    'University Technologies International Inc',\n",
    "    'Heart Institute',\n",
    "    'Sapienza University of Rome',\n",
    "    'University of Montreal',\n",
    "    'Willamette University',\n",
    "    'University of Rochester',\n",
    "    'University of Southern California (USC) Stevens Center for Innovation',\n",
    "    'Northeastern University',\n",
    "    'University of North Carolina',\n",
    "    'University of Massachusetts',\n",
    "    'New York University',\n",
    "    'KU Leuven',\n",
    "    'Keimyung University',\n",
    "    'Salk Institute for Biological Studies',\n",
    "    'Kitasato Institute',\n",
    "    'Helmholtz Zentrum Munchen',\n",
    "    'The Hong Kong Polytechnic University',\n",
    "    'Paris Descartes University',\n",
    "    'University of Konstanz',\n",
    "    'Ohio University',\n",
    "    'Josep Carreras Leukaemia Research Institute',\n",
    "    'Alexander Fleming Biomedical Sciences Research Center',\n",
    "    'Marquette University',\n",
    "    'Lieber Institute for Brain Development (LIBD)',\n",
    "    'Tel Aviv University',\n",
    "    'Istanbul University',\n",
    "    'The City University of New York',\n",
    "    'Pasteur Institute',\n",
    "    'University of Aberdeen',\n",
    "    'University of Kansas',\n",
    "    'Thomas Jefferson University',\n",
    "    'Montana State University',\n",
    "    'University of Nebraska-Lincoln',\n",
    "    'North Carolina State University',\n",
    "    'Columbia University',\n",
    "    'Andres Bello National University',\n",
    "    'Ariel University',\n",
    "    'UNIVERSITY KING ABDULAZIZ',\n",
    "    'Jinan University',\n",
    "    'University of Nantes',\n",
    "    'University of Milano',\n",
    "    'University of Bristol',\n",
    "    'Hefei Institutes of Physical Science',\n",
    "    'Institute for Hepatitis and virus research',\n",
    "    'ETH Zurich',\n",
    "    'University of Illinois Urbana-Champaign',\n",
    "    'Korea Research Institute of Chemical Technology',\n",
    "    'Georgia State University Research Foundation',\n",
    "    'Flemish Institute for Biotechnology',\n",
    "    'Bandeirante University of Sao Paulo',\n",
    "    'UCB',\n",
    "    'Middle Tennessee State University',\n",
    "    'Florida Agricultural and Mechanical University',\n",
    "    'Yale University',\n",
    "    'The University of Toronto Governing Council',\n",
    "    'Chosun University Industry-Academia Cooperation',\n",
    "    'California Institute of Technology',\n",
    "    'Massachusetts Institute of Technology',\n",
    "    'Institute of Molecular Medicine (iMM)',\n",
    "    'Sichuan University',\n",
    "    'University of Texas',\n",
    "    'Shenyang Pharmaceutical University',\n",
    "    'University of Miami',\n",
    "    'The University of Maryland',\n",
    "    'University of Florida Research Foundation',\n",
    "    'Cornell University',\n",
    "    'Hungarian Academy of Sciences',\n",
    "    'University College Cardiff Consultants Ltd',\n",
    "    'Cardiff University',\n",
    "    'Emory University',\n",
    "    'Parker Hughes Institute',\n",
    "    'University of Dayton',\n",
    "    'University of Dundee',\n",
    "    'Harvard College',\n",
    "    'University of London',\n",
    "    'Clemson University',\n",
    "    'French National Institute of Health and Medical Research (Inserm)',\n",
    "    'Texas Tech University System',\n",
    "    'University of Southampton',\n",
    "    'Indiana University Board of Trustees',\n",
    "    'Sanford Burnham Prebys Medical Discovery Institute',\n",
    "    'Ohio State University',\n",
    "    'University of Greenwich',\n",
    "    'University of Mississippi',\n",
    "    'Brandeis University',\n",
    "    'Saint Louis University',\n",
    "    'Icahn School of Medicine at Mount Sinai',\n",
    "    'Drexel University',\n",
    "    'Shanghai Institute of Materia Medica',\n",
    "    'National Health Research Institutes (NHRI)',\n",
    "    'Erasmus University Medical Center',\n",
    "    'Baruch S. Blumberg Institute',\n",
    "    'University of the Basque Country',\n",
    "    'Monash University',\n",
    "    'Heart Research Institute',\n",
    "    'University of Montana',\n",
    "    'Zhejiang University',\n",
    "    'Aarhus University',\n",
    "    'Duquesne University',\n",
    "    'Massey University',\n",
    "    'Albert Einstein College of Medicine',\n",
    "    'Medical University of Warsaw',\n",
    "    'University City',\n",
    "    'University of Chicago',\n",
    "    'Northwest University',\n",
    "    'University of Pittsburgh',\n",
    "    'University of Innsbruck',\n",
    "    'Lille 2 University of Health and Law',\n",
    "    \"Xi'an Jiaotong University\",\n",
    "    'Taipei Medical University',\n",
    "    'University of Oregon',\n",
    "    'University of Leeds',\n",
    "    'Trustees of Boston College',\n",
    "    'Korea University Research and Business Foundation',\n",
    "    'University of Notre Dame',\n",
    "    'Regents of The University of Michigan',\n",
    "    'INRAE',\n",
    "    'University of Arkansas',\n",
    "    'University of Kentucky Research Foundation',\n",
    "    'University of Milan',\n",
    "    'University of South Carolina',\n",
    "    'University of Toyama',\n",
    "    'The Kitasato Institute',\n",
    "    'Chemical Institute of Sarria',\n",
    "    'University of Florence',\n",
    "    'Iowa State University Research Foundation',\n",
    "    'Sanford Burnham Prebys',\n",
    "    'University of Colorado Boulder',\n",
    "    'Duke University',\n",
    "    'Gladstone Institutes',\n",
    "    'Simon Fraser University',\n",
    "    'City University of Hong Kong',\n",
    "    'Howard University',\n",
    "    'University of Rouen Normandy',\n",
    "    'Huazhong University of Science and Technology',\n",
    "    'Yantai University',\n",
    "    'Johns Hopkins University',\n",
    "    'Oslo University Hospital',\n",
    "    'Catalan Institution for Research and Advanced Studies (ICREA)',\n",
    "    'Chinese Academy of Medical Sciences',\n",
    "    'Seattle Childrens Research Institute',\n",
    "    'The Trustees Of Columbia University',\n",
    "    'University of Saskatchewan',\n",
    "    'The University of Tokyo',\n",
    "    'University of Texas System',\n",
    "    'University of Nottingham',\n",
    "    'University of Bath',\n",
    "    'Ewha University-Industry Collaboration Foundation',\n",
    "    'Case Western Reserve University',\n",
    "    'Zucker Institute for Innovation Commercialization',\n",
    "    'PSL Research University',\n",
    "    'University of Colorado Board of Regents',\n",
    "    'University of Michigan',\n",
    "    'Shandong University',\n",
    "    'Korea Institute of Science and Technology',\n",
    "    'Parkinsons Institute',\n",
    "    'Whitehead Institute',\n",
    "    'University of Alberta',\n",
    "    'Shanghai institute of Organic Chemistry',\n",
    "    'University of Florida',\n",
    "    'The University of Virginia',\n",
    "    'University of Oxford',\n",
    "    'North Dakota State University Research Foundation',\n",
    "    'Institute for One World Health',\n",
    "    'Imam Abdulrahman Bin Faisal University',\n",
    "    'Institute of Advanced Technology and Research and Development,',\n",
    "    'East China University of Science and Technology',\n",
    "    'Georgetown University',\n",
    "    'Washington University in St. Louis',\n",
    "    'University of Windsor',\n",
    "    'Arizona State University',\n",
    "    'University of Pennsylvania',\n",
    "    'University of North Carolina at Chapel Hill at Chapel Hill',\n",
    "    'Indian Institute Of Technology',\n",
    "    'The Rockefeller University',\n",
    "    'University-industry Cooperation Group of Kyung Hee University',\n",
    "    'Oxford University Innovation',\n",
    "    'National Institutes for Quantum Science and Technology (QST)',\n",
    "    'Nankai University',\n",
    "    'Temple University',\n",
    "    'Yamaguchi University',\n",
    "    'Rutgers University',\n",
    "    'The Translational Genomics Research Institute (TGen)',\n",
    "    'Illinois Board of Trustees',\n",
    "    'Institute of Biochemistry and Biophysics',\n",
    "    'Buck Institute for Research on Aging',\n",
    "    'Wake Forest University',\n",
    "    'University of Limerick',\n",
    "    'University of North Carolina at Greensboro',\n",
    "    'University of Utah',\n",
    "    'University of Minnesota',\n",
    "    'University of Connecticut',\n",
    "    'University of Melbourne',\n",
    "    'University of Vienna',\n",
    "    'Walter and Eliza Hall Institute of Medical Research',\n",
    "    'Gunma University',\n",
    "    'Institute of Botany of the Czech Academy of Sciences',\n",
    "    'National University of Singapore',\n",
    "    'University of Lille',\n",
    "    'China Central Research Academy,',\n",
    "    'Keio University',\n",
    "    'Peking University',\n",
    "    'Georgia Tech Research Institute',\n",
    "    'University of Antwerp',\n",
    "    'University of Kentucky',\n",
    "    'Shanghai Institute of Materia Medica Chinese Academy of Sciences',\n",
    "    'University of Zurich',\n",
    "    'The Institute of Cancer Research',\n",
    "    'Schepens Eye Research Institute',\n",
    "    'Broad Institute',\n",
    "    'University of Toronto',\n",
    "    'Institute for Research in Biomedicine',\n",
    "    'Chiba University',\n",
    "    'University of Milano-Bicocca',\n",
    "    'University of North Carolina at Chapel Hill',\n",
    "    'STC University of New Mexico',\n",
    "    'Stanford University',\n",
    "    'Allegheny College',\n",
    "    'The Netherlands Cancer Institute',\n",
    "    'Nagoya University',\n",
    "    'The University of Oklahoma',\n",
    "    'Regents of the University of California',\n",
    "    'King Abdulaziz University',\n",
    "    'Hannover Medical School',\n",
    "    'Wistar Institute',\n",
    "    'University of Birmingham',\n",
    "    'Pusan National University',\n",
    "    'Italian Institute of Technology',\n",
    "    'Guangzhou Institute of Biomedicine and Health',\n",
    "    'Fudan University',\n",
    "    'Research Foundation of State University of New York',\n",
    "    'Kyung Hee University-Industry Cooperation',\n",
    "    'University of Iowa Research Foundation',\n",
    "    'Grande ABC University',\n",
    "    'University of Eastern Piedmont',\n",
    "    'Ottawa Hospital Research Institute',\n",
    "    'Mie University',\n",
    "    'The University of New Mexico',\n",
    "    'University of Leicester',\n",
    "    'Kaohsiung Medical University',\n",
    "    'Autonomous University of Barcelona',\n",
    "    'Claude Bernard University Lyon 1',\n",
    "    'Brown University',\n",
    "    'University College Cork',\n",
    "    'Soochow University',\n",
    "    'University of Southern California',\n",
    "    'National Institute of Advanced Industrial Science and technology (AIST)',\n",
    "    'Dr. Wolff Group',\n",
    "    'Mangosuthu University of Technology',\n",
    "    'University of Washington',\n",
    "    'Muroran Institute of Technology',\n",
    "    'Human BioMolecular Research Institute',\n",
    "    'Tulane University',\n",
    "    'Wayne State University',\n",
    "    'National University Corporation Kyushu University',\n",
    "    'IMDEA Food Institute',\n",
    "    'University of Helsinki',\n",
    "    'Korea Research Institute of Bioscience & BioTechnology',\n",
    "    'University of Cape Town',\n",
    "    'Technical University of Denmark',\n",
    "    'Sao Paulo Research Foundation',\n",
    "    'University of Queensland',\n",
    "    'McMaster University',\n",
    "    'University of Illinois',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e89ae2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_assignee_rank(df: pd.DataFrame):\n",
    "    \"\"\"Create assignee dataframe based on the no.of patent documents contributed.\"\"\"\n",
    "    \n",
    "    patent_dict = defaultdict(lambda: defaultdict(set))\n",
    "    \n",
    "    for patent_idx, date, assignee, org, is_aquired, person_name in tqdm(\n",
    "        df[['patent_id', 'date', 'assignee', 'organization_name', 'acquired_by', 'person']].values\n",
    "    ):\n",
    "        year = date.split(\"-\")[0]\n",
    "        \n",
    "        if pd.notna(is_aquired) or pd.notna(person_name):  # Skip non organisation\n",
    "            continue\n",
    "        \n",
    "        if org in UNIVERSITY_ASSIGNEES:\n",
    "            patent_dict['university'][year].add(patent_idx)\n",
    "        else:\n",
    "            patent_dict['industy'][year].add(patent_idx)\n",
    "        \n",
    "        \n",
    "    patent_data = []\n",
    "    for assignee_type, year_based_dict in patent_dict.items():\n",
    "        # Get total patent count\n",
    "        all_patents = set()\n",
    "        for patents in year_based_dict.values():\n",
    "            all_patents.update(patents)\n",
    "        \n",
    "        for year, year_patents in year_based_dict.items():\n",
    "            patent_data.append({\n",
    "                'assignee_type': assignee_type,\n",
    "                'total_patent_count': len(all_patents),\n",
    "                'year': int(year),\n",
    "                'annual_patent_count': len(year_patents)\n",
    "            })\n",
    "        \n",
    "    return pd.DataFrame(patent_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c90742d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 17375/17375 [00:00<00:00, 335100.73it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assignee_type</th>\n",
       "      <th>total_patent_count</th>\n",
       "      <th>year</th>\n",
       "      <th>annual_patent_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>industy</td>\n",
       "      <td>317</td>\n",
       "      <td>2000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   assignee_type  total_patent_count  year  annual_patent_count\n",
       "19       industy                 317  2000                    6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_disease_assignee_rank = create_assignee_rank(rare_disease_data)\n",
    "rare_disease_assignee_rank.sort_values(\n",
    "    by=['total_patent_count', 'year'], ascending=[False, True], inplace=True\n",
    ")\n",
    "rare_disease_assignee_rank.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d8da3cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 75616/75616 [00:00<00:00, 358300.50it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assignee_type</th>\n",
       "      <th>total_patent_count</th>\n",
       "      <th>year</th>\n",
       "      <th>annual_patent_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>industy</td>\n",
       "      <td>8622</td>\n",
       "      <td>2000</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   assignee_type  total_patent_count  year  annual_patent_count\n",
       "33       industy                8622  2000                  143"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alzheimers_disease_assignee_rank = create_assignee_rank(alzheimer_disease_data)\n",
    "alzheimers_disease_assignee_rank.sort_values(\n",
    "     by=['total_patent_count', 'year'], ascending=[False, True], inplace=True\n",
    ")\n",
    "alzheimers_disease_assignee_rank.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64688705",
   "metadata": {},
   "source": [
    "### Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "006e4741",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_barplot(df1: pd.DataFrame, df2: pd.DataFrame):\n",
    "    sns.set(style='white')\n",
    "    \n",
    "    plt.figure(figsize=(10, 5))\n",
    "    \n",
    "    plt.subplots_adjust(wspace=0)\n",
    "    \n",
    "    plt.subplot(2,1,1)\n",
    "    plt.bar(\n",
    "        df1['year'], df1['annual_patent_count'],\n",
    "        width=0.5, alpha=0.6, color='#8a853b'\n",
    "    )\n",
    "    \n",
    "    plt.xlabel('Year', size=12)\n",
    "    plt.ylabel('Number of patents', size=12)\n",
    "    plt.xticks(fontsize=10)\n",
    "    plt.yticks(fontsize=10)\n",
    "    plt.ylim(0, 45)\n",
    "    \n",
    "    plt.title(\n",
    "        'Patenting landscape within organisations', \n",
    "        horizontalalignment='center', size=14,\n",
    "        verticalalignment='center', weight='bold',\n",
    "        pad=30\n",
    "    )\n",
    "    \n",
    "    k = df1[['year', 'annual_patent_count']].groupby('year').sum().reset_index()\n",
    "    plt.plot(k['year'], k['annual_patent_count'], marker='.', color='black', ms=5)\n",
    "\n",
    "    plt.text(0, 1.1, string.ascii_uppercase[0],  \n",
    "        size=15, weight='bold', horizontalalignment='center',\n",
    "        verticalalignment='center',\n",
    "        transform = plt.gca().transAxes)\n",
    "    \n",
    "    \"\"\"Subplot 2\"\"\"\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.bar(\n",
    "        df2['year'], df2['annual_patent_count'],\n",
    "        width=0.5, alpha=0.6\n",
    "    )\n",
    "\n",
    "    plt.xlabel('Year', size=12)\n",
    "    plt.ylabel('Number of patents', size=12)\n",
    "    plt.xticks(fontsize=10)\n",
    "    plt.yticks(fontsize=10)\n",
    "    \n",
    "    m = df2[['year', 'annual_patent_count']].groupby('year').sum().reset_index()\n",
    "    plt.plot(m['year'], m['annual_patent_count'], marker='.', color='black', ms=5)\n",
    "    \n",
    "    plt.text(0, 1.1, string.ascii_uppercase[1],  \n",
    "        size=15, weight='bold', horizontalalignment='center',\n",
    "        verticalalignment='center',\n",
    "        transform = plt.gca().transAxes)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    plt.savefig(\n",
    "        f'{FIG_DIR}/supplementary_figure_2.png',\n",
    "        dpi=400,\n",
    "        bbox_inches='tight', \n",
    "    )\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d8d3b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_barplot(rare_disease_assignee_rank, alzheimers_disease_assignee_rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34b079fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "patent_kg",
   "language": "python",
   "name": "patent_kg"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}